Implementation of Convolutional Neural Network Method to Detect Diseases in Tomato Leaf Image
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Abstract
Tomato (Lycopersicum esculentum Mill) is one of the leading commodities that has the potential to be a contributor to exports. One of the main causes of decreased production of tomato plants, namely the emergence of various diseases. Plants are said to be affected by disease if there are changes in all or part of the plant organs that cause disruption of daily physiological activities. This study will use deep learning methods and Convolutional Neural Network (CNN) algorithms to determine disease in tomato plants through leaves. The CNN training model will be carried out using the Python and carried out on the Google Colab platform, while the Android-based application development will use Android Studio. Tests have been carried out by implementing various test scenarios, namely testing with image sources from the gallery and image sources directly from the camera. The result is an application that is built quite reliably with an accuracy of testing on images from the gallery of 94% and 80% accuracy for testing using images taken directly from the camera.
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